Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria

Baouche, Rafik; Baddari, Kamel

doi:10.1016/j.ejpe.2016.10.013

Cited by 97 publications

(27 citation statements)

References 10 publications

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“…Many algorithms have been adopted for lithofacies/electrofacies classification, such as linear discriminant analysis (Al-Mudhafar 2014, 2015aLee and DattaGupta 1999;Rafik and Kamel 2016), multinomial logistic regression (Al-Mudhafar 2014; Tang et al 2004), neural networks (Avseth and Mukerji 2002;Tang 2008;Wong et al 1995), kernel support vector machine (Al-Mudhafar 2015a, b, 2017a, and principal component analysis (Adoghe et al 2011). All these algorithms predict the discrete and continuous probability distributions of facies.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms include multiple linear regression (Dahraj and Bhutto 2014;Mohaghegh et al 1997;Xue et al 1997), generalized additive modeling (Al-Mudhafar and Mohamed 2015; AlMudhafar and Bondarenko 2015; Lee et al 2002;Rafik and Kamel 2016), multivariate adaptive regression splines (Al-Mudhafar and Al-Khazraji 2016;Xie 2008), neural networks (Lee and Datta-Gupta 1999;Lee et al 2002;Mohaghegh et al 1997), fuzzy logic (Nashawi and Malallah 2009), and support vector regression (Al-Anazi and Gates 2011).…”

Section: Introductionmentioning

confidence: 99%

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Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Al‐Mudhafar

2017

J Petrol Explor Prod Technol

141

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In this paper, an integrated procedure was adopted to obtain accurate lithofacies classification to be incorporated with well log interpretations for a precise core permeability modeling. Probabilistic neural networks (PNNs) were employed to model lithofacies sequences as a function of well logging data in order to predict discrete lithofacies distribution at missing intervals. Then, the generalized boosted regression model (GBM) was used as to build a nonlinear relationship between core permeability, well logging data, and lithofacies. The well log interpretations that were considered for lithofacies classification and permeability modeling are neutron porosity, shale volume, and water saturation as a function of depth; however, the measured discrete lithofacies types are sand, shaly sand, and shale. Accurate lithofacies classification was achieved by the PNN as the total percent correct of the predicted discrete lithofacies was 95.81%. In GBM results, root-mean-square prediction error and adjusted R-square have incredible positive values, as there was an excellent matching between the measured and predicted core permeability. Additionally, the GBM model led to overcome the multicollinearity that was available between one pair of the predictors. The efficiency of boosted regression was demonstrated by the prediction matching of core permeability in comparison with the conventional multiple linear regression (MLR). GBM led to much more accurate permeability prediction than the MLR.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Al‐Mudhafar

2017

J Petrol Explor Prod Technol

141

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“…Rafik et al predicted reservoir permeability using the nonparametric multivariate regression and neural network [19]. They applied well logging data of an oil reservoir in Algeria.…”

Section: Literature Reviewmentioning

confidence: 99%

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

et al. 2017

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Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

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“…Sometimes, sand or/and sandstone reservoir contains the high permeability for the coarser grain at a low porosity and the presence of fine grain causes the low permeability at the high porosity (Mortensen et al 1998;Nelson 1994;Beard and Weyl 1973;Holmes et al 2009). Many investigators (Archie 1942;Tixier 1949;Wyllie and Rose 1950;Pirson 1963;Timur 1968;Coats and Dumanoir 1974;Schlumberger Ltd 1987;Kapadia and Menzie 1985;Bloch 1991;Ahmad et al 1991) attempted to capture the relation of permeability function with model. However, these studies 1 3 only contributed to the better understanding of the factor controlling permeability but not the actual relationship.…”

Section: Introductionmentioning

confidence: 99%

Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

Singh

2018

J Petrol Explor Prod Technol

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Permeability is an important petrophysical parameter of hydrocarbon reservoirs for oil and gas production. Formation permeability is often measured in the laboratory test using core samples. However, when few core samples are available to calculate the permeability in the field, estimation of permeability becomes a challenging task. In study area, the Chandmari field of upper Assam-Arakan basin with the availability of only seven core samples and conventional logs such as density, porosity, resistivity and gamma ray data from few wells, the estimation of permeability becomes a difficult task. Therefore, in the present study an attempt is made to estimate the permeability from well log and core data using Buckles' method approach in Langpar and Lakadong + Therria sanstone reservoir of Eocene-Paleocene geologic age in the field under the assumption and geological support that reservoirs in the study area are clean sand having very less shale control and are homogenous reservoir with little/no heterogeneity. In this study, petrophysical evaluation from log data and that from core data are integrated for the analysis of the reservoir characteristics. The relationship between porosity and water saturation which is required to distinguish mobile from capillary bound water or irreducible water saturation is used to estimate the irreducible water saturation. The estimated irreducible water saturation which is an essential parameter for water cut and permeability estimation is used for estimating the permeability in the field. The estimated permeability in the reservoirs using Buckles' method ranging from 1500 to 4554.38 mD is well matched with the permeability estimated from core sample. The estimated permeability results suggest that the oil reservoir has the higher permeability than the gas reservoir. The permeability estimation relationship can further be used for the estimation of permeability in the inter-well region of Chandmari oil field.

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Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria

Cited by 97 publications

References 10 publications

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin

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